Generally, PID control systems are used for controlling the cold water outlet temperature of absorption refrigerators. However, observation of the absorption refrigerators provides many items of data including the temperatures at the cold water outlet, cold water inlet, cooling water outlet and cooling water inlet, the temperature of the generator, etc. If all of these items of data are used as input variables for PID control to adjust the degree of opening of the fuel control valve, there arises the problem that the control system becomes extremely complex. The control system further has the problem of being lower in precision of control and responsiveness than when the opening degree of the fuel control valve is manually adjusted by skilled operators based on the heuristic knowledge and with reference to these items of data measured. These problems become more serious in the case of absorption refrigerators having two generators for high and low temperatures.
In recent years, attempts have been made to apply fuzzy logic to automatic control to realize exquisite control comparable to the manual control by skilled operators. For example, U.S. Pat. No. 4,842,342 discloses a system for controlling the operation of motor vehicle brakes by fuzzy logic control.
Accordingly, it appears feasible to control the cold water outlet temperature of absorption refrigerators by fuzzy logic control. The application of fuzzy logic control to absorption refrigerators nevertheless involves the following problem.
In fuzzy logic control, the experience or knowledge quantitatively acquired by skilled operators is expressed in the form of IF (antecedent)-THEN (consequent) to prepare control rules (hereinafter referred to merely as "rules") for use in fuzzy reasoning. In many cases, the antecedents of rules comprise a plurality of input variables. For example, suppose input variables (antecedent variable) is A, B and C, an output variable (consequent variable) is D, and fuzzy variables (membership functions) are represented by five fuzzy labels, i.e., NB (Negative Big), NS (Negative Small), ZR (Zero), PS (Positive Small) and PB (Positive Big). One rule is then expressed as follows. EQU Rule 1: IF A is PB AND B is ZR AND C is PB, THEN D is ZR.
Accordingly, if the three input variables each have the five fuzzy labels, the total number of rules is 5.sup.3 =125. In the case of the control system for an absorption refrigerator having a high temperature generator and a low temperature generator, the number of input variables is at least 5, hence a very large number of rules. If these rules are all to be described to make fuzzy reasoning, the arithmetic operation requires a long period of time, and if the operation time exceeds the control period, control becomes impossible.
A reduction in the number of membership functions decreases the number of rules, whereas rough control will then result.
Further in the fuzzy logic control of absorption refrigerators, it is required to make the cold water outlet temperature free from offset during the stabilized period of control and to effectively inhibit the influence of interferences.
Further in the conventional method of fuzzy logic control, the antecedent membership functions for formulating rules are defined in the range of 0 to 1 in grade, and different input variables exert the same degree of influence on the control input. However, the experience of skilled operators indicates that the degree of influence of the input variable on the controlled input varies from variable to variable. This heuristic knowledge must be utilized in fuzzy logic control.